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Magnetic bacteria exhibit a directed movement called magnetotaxis, driven by structures called magnetosomes. These magnetosomes consist of chains of magnetic particles made of either magnetite (Fe₃O₄) or greigite (Fe₃S₄) and are organized in a linear conformation by a protein scaffold within invaginations of the cell membrane. The bacteria align along the north–south magnetic field lines, much like a compass needle. They are typically microaerophilic or anaerobic...
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Updated: Oct 1, 2025

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures
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Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments.

Ahmad Ataka1, Hak-Keung Lam2, Kaspar Althoefer3

  • 1Signal Processing Laboratory, Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.

Frontiers in Robotics and AI
|March 7, 2022
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Summary

This study introduces a novel magnetic-field-inspired navigation method for robots. This approach ensures real-time obstacle avoidance in complex environments without prior mapping, enhancing robotic adaptability.

Keywords:
magnetic-field-inspired navigationmotion controlobstacle advoidancepath planning for manipulatorsreactive navigation

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Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Control
  • Artificial Intelligence

Background:

  • Robotic applications have expanded from industrial settings to complex, unstructured environments.
  • Real-time obstacle avoidance is a critical challenge for robots operating in these new domains.
  • Existing navigation systems often require prior environmental knowledge or have limitations in handling diverse obstacle types.

Purpose of the Study:

  • To propose a novel magnetic-field-inspired navigation method for robots.
  • To demonstrate guaranteed obstacle avoidance for various obstacle geometries (convex and non-convex).
  • To ensure goal convergence for point-like robots in specific environmental configurations.

Main Methods:

  • A magnetic-field-inspired algorithm is developed for robot navigation.
  • The method relies solely on temporally and spatially local environmental sensor data.
  • No prior information about the environment, obstacles, or their geometry is required.

Main Results:

  • The proposed method guarantees obstacle avoidance for both convex and non-convex obstacles.
  • Goal convergence is achieved for point-like robots in environments with convex and non-maze concave obstacles.
  • The algorithm was successfully validated through simulations and experiments on diverse robotic platforms, including the Baxter robot.

Conclusions:

  • The magnetic-field-inspired navigation method offers significant advantages over alternative systems.
  • It provides a robust and adaptable solution for real-time obstacle avoidance in complex 2D and 3D environments.
  • The method's versatility allows implementation across a wide range of robotic platforms.